Abstract

Ternary content-addressable memories (TCAMs) are used to design high-speed search engines. TCAM is implemented on application-specific integrated circuit (native TCAMs) and field-programmable gate array (FPGA) (static random-access memory (SRAM)-based TCAMs) platforms but both have the drawback of high power consumption. This paper presents a pre-classifier-based architecture for an energy-efficient SRAM-based TCAM. The first classification stage divides the TCAM table into several sub-tables of balanced size. The second SRAM-based implementation stage maps each of the resultant TCAM sub-tables to a separate row of configured SRAM blocks in the architecture. The proposed architecture selectively activates at most one row of SRAM blocks for each incoming TCAM word. Compared with the existing SRAM-based TCAM designs on FPGAs, the proposed design consumes significantly reduced energy as it activates a part of SRAM memory used for lookup rather than the entire SRAM memory as in the previous schemes. We implemented the proposed approach sample designs of size 512 × 36 on Xilinx Virtex-6 FPGA. The experimental results showed that the proposed design achieved at least three times lower power consumption per performance than other SRAM-based TCAM architectures.

Highlights

  • Ternary content-addressable memory (TCAM) selects a word among stored ternary data based on its contents

  • TCAM-based search engines play an important role in networking routers

  • Researchers have proposed several static random-access memory (SRAM)-based TCAM designs on re-configurable hardware

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Summary

Introduction

Ternary content-addressable memory (TCAM) selects a word among stored ternary data based on its contents. The demand for a high-speed flexible (re-configurable) and adaptable (easy for integration) TCAM configurations renders the embedded memories BRAMs on modern SRAM-based FPGAs attractive for the design of TCAMs. FPGAs implement TCAM using SRAM, by addressing SRAM with TCAM contents, and stores information for all data of TCAM table. The SRAM-based TCAM design methodologies presented in recent works [13,17] consumed 2.5 W and 3.2 W to implement 89 kb and 150 kb TCAM tables using BRAMs on FPGA, respectively. The demand for a low power configurable and easy to integration TCAM design on FPGA makes the use of pre-classification approach practical for designing an energy-efficient SRAM-based TCAM (EE-TCAM) It works as follows: First, a TCAM table is partitioned into several sub-tables of balanced size in the classification stage.

Related Work
Proposed Classification Scheme for TCAM Table
Proposed Architecture
Update Operation
FPGA Implementation Results & Performance Evaluation
Scalability of EE-TCAM
Power Consumption
Power Consumption per Performance
Conclusions
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